Bowstring-based dual-threshold computation method for adaptive Canny edge detector

This paper proposed a novel dual-threshold computation method of Canny edge detector based on gradient magnitude histogram (GMH), targeting with the adaptive acquisition of low-/high-threshold for unimodal hysteresis thresholding. With the introduction of the bowstring concept, which accurately measures the tendency of the GMH on the whole, the dual-threshold computation is implemented by adaptive-searching two tangent points with transitional characteristics. This skillful algorithm of the dual-threshold computation method is further evaluated by using the receiver operating characteristics (ROC) curve evaluation method. The detailed comparison to the Otsu's method is presented and demonstrates the reliability and robust performance of the proposed dual-threshold computation method.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Francisco José Madrid-Cuevas,et al.  Unimodal thresholding for edge detection , 2008, Pattern Recognit..

[3]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Francisco José Madrid-Cuevas,et al.  On candidates selection for hysteresis thresholds in edge detection , 2009, Pattern Recognit..

[5]  Azriel Rosenfeld,et al.  Histogram concavity analysis as an aid in threshold selection , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Francisco José Madrid-Cuevas,et al.  Solving the process of hysteresis without determining the optimal thresholds , 2010, Pattern Recognit..

[7]  Yitzhak Yitzhaky,et al.  A Method for Objective Edge Detection Evaluation and Detector Parameter Selection , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Yang Zhang,et al.  The Bayesian Operating Point of the Canny Edge Detector , 2006, IEEE Transactions on Image Processing.

[9]  Sean Dougherty,et al.  Edge Detector Evaluation Using Empirical ROC Curves , 2001, Comput. Vis. Image Underst..

[10]  Liu Ning,et al.  An Improved Adaptive Threshold Canny Edge Detection Algorithm , 2012, 2012 International Conference on Computer Science and Electronics Engineering.

[11]  W X Wang,et al.  Parameter optimal determination for canny edge detection , 2011 .

[12]  Francisco José Madrid-Cuevas,et al.  A novel histogram transformation to improve the performance of thresholding methods in edge detection , 2011, Pattern Recognit. Lett..

[13]  Francisco José Madrid-Cuevas,et al.  Determining Hysteresis Thresholds for Edge Detection by Combining the Advantages and Disadvantages of Thresholding Methods , 2010, IEEE Transactions on Image Processing.

[14]  J. Kittler,et al.  Adaptive estimation of hysteresis thresholds , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Paul L. Rosin Unimodal thresholding , 2001, Pattern Recognit..

[16]  Dehua Li,et al.  An Adaptive Canny Edge Detector using Histogram Concavity Analysis , 2011 .

[17]  Rafael Muñoz-Salinas,et al.  A novel method to look for the hysteresis thresholds for the Canny edge detector , 2011, Pattern Recognit..

[18]  Le-Nan Wu,et al.  An adaptive threshold for the Canny Operator of edge detection , 2010, 2010 International Conference on Image Analysis and Signal Processing.

[19]  Peter Rockett,et al.  Performance assessment of feature detection algorithms: a methodology and case study on corner detectors , 2003, IEEE Trans. Image Process..

[20]  N. Otsu A threshold selection method from gray level histograms , 1979 .